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SEA-HELM: Southeast Asian Holistic Evaluation of Language Models
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With the rapid emergence of novel capabilities in Large Language Models (LLMs), the need for rigorous multilingual and multicultural benchmarks that are integrated has become more pronounced. Though existing LLM benchmarks are capable of evaluating specific capabilities of LLMs in English as well as in various mid- to low-resource languages, including those in the Southeast Asian (SEA) region, a comprehensive and culturally representative evaluation suite for the SEA languages has not been developed thus far. Here, we present SEA-HELM, a holistic linguistic and cultural LLM evaluation suite that emphasises SEA languages, comprising five core pillars: (1) NLP Classics, (2) LLM-specifics, (3) SEA Linguistics, (4) SEA Culture, (5) Safety. SEA-HELM currently supports Filipino, Indonesian, Tamil, Thai, and Vietnamese. We also introduce the SEA-HELM leaderboard, which allows users to understand models' multilingual and multicultural performance in a systematic and user-friendly manner. We make the SEA-HELM evaluation code publicly available.
Forward citations
Cited by 2 Pith papers
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Mangosteen: An Open Thai Corpus for Language Model Pretraining
An open 47B-token Thai pre-training corpus and a Thai-adapted data cleaning pipeline, with ablations showing quality gains and an 8B model that improves on Thai benchmarks.
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IberBench: LLM Evaluation on Iberian Languages
A community-run benchmark for Iberian languages shows that LLMs underperform on industry-relevant NLP tasks and on Basque and Galician relative to fundamental tasks and other Iberian languages.
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